DMNet: A Network Architecture Using Dilated Convolution and Multiscale Mechanisms for Spatiotemporal Fusion of Remote Sensing Images

Li, Weisheng; Zhang, Xiayan; Peng, Yidong; Dong, Meilin

doi:10.1109/jsen.2020.3000249

Cited by 26 publications

(14 citation statements)

References 37 publications

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“…Compared with single-stage target detection algorithms such as the YOLO series, our method improves 1.2%-5% mAP with significant results. Our method improves 1%-4% mAP compared to popular two-stage target detection algorithms, including the airborne remote sensing detection model DMnet [31] and HRNet [32] with high-resolution feature maps. The detection accuracy of our model is slightly higher than that of Swin-Transformer [33], which has superior performance.…”

Section: Verification Experimentsmentioning

confidence: 98%

SACNet: Switchable Atrous Convolution Network Based on Diffusion Model for Small Object Detection from UAV Images

Wang,

Zhu,

2024

Preprint

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Small object detection has always been one of the most challenging tasks in the computer vision. Up to now, a prior bounding box is often applied to Unmanned Aerial Vehicle (UAV) image object detection. However, anchors need to be pre-set and not optimal for training data in many object detection algorithms. In 2022, the Diffusion Model was introduced in object detection method, in which the random boxes are employed. Inspired by this method, we propose a Switchable Atrous Convolution Network based on the Diffusion Model. First of all, the normalized Wasserstein distance (NWD) is led into measuring the similarity between the prediction box and the ground truth box. The purpose is to eliminate the influence of positional sensitivity on the matching between the predicted box and the ground truth box. Secondly, a Switchable Atrous Convolution (SAC) with different atrous rates and shared weights is introduced, and similar features at different scales are extracted. This can solve the challenge of significant scale variations of the same target in the image. Finally, Pixel shuffle and Upsampling operations are employed in the feature extraction backbone network to enhance the model's ability to detect small targets. The optimal results of 27.89% mAP on the VisDrone dataset and 8.42% mAP on the Tinyperson dataset demonstrate the effectiveness of the proposed model.

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Section: Verification Experimentsmentioning

confidence: 98%

SACNet: Switchable Atrous Convolution Network Based on Diffusion Model for Small Object Detection from UAV Images

Wang,

Zhu,

2024

Preprint

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“…CNNs have been applied to spatiotemporal image fusion deep learning methods [28,29], and the fusion performance has been improved. The deep CNN for spatiotemporal fusion (STFDCNN) [30] reconstructs spatial resolution images from temporal resolution images using a spatiotemporal fusion method involving deep CNNs; notably, nonlinear mapping super-resolution-based CNNs are less efficient than the sparse representation method.…”

Section: Related Workmentioning

confidence: 99%

STF-EGFA: A Remote Sensing Spatiotemporal Fusion Network with Edge-Guided Feature Attention

Fang

Tang

et al. 2022

Remote Sensing

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Spatiotemporal fusion in remote sensing plays an important role in Earth science applications by using information complementarity between different remote sensing data to improve image performance. However, several problems still exist, such as edge contour blurring and uneven pixels between the predicted image and the real ground image, in the extraction of salient features by convolutional neural networks (CNNs). We propose a spatiotemporal fusion method with edge-guided feature attention based on remote sensing, called STF-EGFA. First, an edge extraction module is used to maintain edge details, which effectively solves the boundary blurring problem. Second, a feature fusion attention module is used to make adaptive adjustments to the extracted features. Among them, the spatial attention mechanism is used to solve the problem of weight variation in different channels of the network. Additionally, the problem of uneven pixel distribution is addressed with a pixel attention (PA) mechanism to highlight the salient features. We transmit the different features extracted by the edge module and the encoder to the feature attention (FA) module at the same time after the union. Furthermore, the weights of edges, pixels, channels and other features are adaptively learned. Finally, three remote sensing spatiotemporal fusion datasets, Ar Horqin Banner (AHB), Daxing and Tianjin, are used to verify the method. Experiments proved that the proposed method outperformed three typical comparison methods in terms of the overall visual effect and five objective evaluation indexes: spectral angle mapper (SAM), peak signal-to-noise ratio (PSNR), spatial correlation coefficient (SCC), structural similarity (SSIM) and root mean square error (RMSE). Thus, the proposed spatiotemporal fusion algorithm is feasible for remote sensing analysis.

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“…Cheng et al [15] presents a method to improve the speed and accuracy rate for space robot visual target recognition based on illumination and affine invariant feature extraction and to reduce the effect of light and occlusion on target recognition. Li et al [16] proposed a network framework (DMNet) incorporating dilation convolution and multi-scale mechanisms. The algorithm extracts image contextual information using the multi-scale mechanism and extracts small detail features using dilation convolution, which effectively improves the recognition performance of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

EHDC: enhanced dilated convolution framework for underwater blurred target recognition

Cai

Qin

2022

Robotica

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The autonomous underwater vehicle (AUV) has a problem with feature loss when recognizing small targets underwater. At present, algorithms usually use multi-scale feature extraction to solve the problem, but this method increases the computational effort of the algorithm. In addition, low underwater light and turbid water result in incomplete information on target features. This paper proposes an enhanced dilated convolution framework (EHDC) for underwater blurred target recognition. Firstly, this paper extracts small target features through hybrid dilated convolution networks, increasing the perceptive field of the algorithm without increasing the computational power of the algorithm. Secondly, the proposed algorithm learns spatial semantic features through an adaptive correlation matrix and compensates for the missing features of the target. Finally, this paper fuses spatial semantic features and visual features for the recognition of small underwater blurred targets. Experiments show that the proposed method improves the recognition accuracy by 1.04% compared to existing methods when recognizing small underwater blurred targets.

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DMNet: A Network Architecture Using Dilated Convolution and Multiscale Mechanisms for Spatiotemporal Fusion of Remote Sensing Images

Cited by 26 publications

References 37 publications

SACNet: Switchable Atrous Convolution Network Based on Diffusion Model for Small Object Detection from UAV Images

SACNet: Switchable Atrous Convolution Network Based on Diffusion Model for Small Object Detection from UAV Images

STF-EGFA: A Remote Sensing Spatiotemporal Fusion Network with Edge-Guided Feature Attention

EHDC: enhanced dilated convolution framework for underwater blurred target recognition

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